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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Pengabdian Masyarakat (ABDIRA)
Hayyun Lisdiana
Universitas Negeri Jakarta
Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Nursing Social Sciences Other

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Penguatan Literasi Energi Terbarukan Model Sel Bahan Bakar Hidrogen bagi Guru MGMP Kimia DKI Jakarta Afrizal Afrizal; Hayyun Lisdiana; Roni Adi Wijaya; Fitri Ramadhani; Siti Azizah; Laurensia Laurensia; Imam Hanafi
Jurnal Pengabdian Masyarakat (ABDIRA) Vol 6, No 2 (2026): Abdira, April
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdira.v6i2.1801

Abstract

This community service program aims to enhance renewable energy literacy and improve the skills of chemistry teachers at Madrasah Aliyah (MA) in DKI Jakarta in developing a hydrogen fuel cell model based on polymer electrolyte membrane (PEMFC) technology. The training was conducted in three stages: preliminary, implementation, and evaluation. The initial stage involved a needs analysis and data collection on teachers’ understanding of fuel cell technology. The implementation stage consisted of lectures, discussions, simulations, assignments, and direct observation of participants’ practical skills in designing the fuel cell model. The evaluation stage assessed the training materials, instructors, and participants to determine the program’s effectiveness. The results showed a significant improvement in teachers’ conceptual understanding and practical competence in integrating hydrogen fuel cell technology into chemistry learning. This program is expected to foster innovative teachers capable of utilising renewable energy technology in classroom practice and improving the quality of students’ academic assessment.
Comparative study of pre-trained CNN models for multiclass fault detection in solar panels Karli Eka Setiawan; Marvel Martawidjaja; Hayyun Lisdiana
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11191

Abstract

Solar power as renewable energy can be an alternative to fossil-based power where it is carbonless, environmentally friendly, and combats climate change. In solar panel systems, manual assessment by personnel is time-demanding and prone to human error, necessitating automated solutions. The implementation of smart systems that can automatically detect objects that hinder the solar panel from receiving solar energy can be very helpful in reducing the potential threat of decreasing performance in power generation. This study proposes a convolutional neural networks (CNN)-based image classification approach to automatically identify common solar panel conditions using visual data. The dataset used was a public dataset titled “Solar Panel Images: Clean and Faulty Images”, obtained from Kaggle, containing six classes for multiclass classification. The most effective pre-trained CNN-based deep learning model for uncovering issues in solar panels was inception-V3, achieving an overall accuracy of 91% and the highest F1-score in four categories: clean, electrical damage, physical damage, and snow coverage. These outcomes confirm the potential of implementing deep learning image classification for enhancing solar panel maintenance and monitoring systems in real-world applications.
Co-Authors Afrizal Afrizal Fitri Ramadhani Imam Hanafi Karli Eka Setiawan Laurensia Laurensia Marvel Martawidjaja Roni Adi Wijaya Siti Azizah